The subject invention pertains to the art of color management and image/text printing or display systems, and is especially applicable to a method and apparatus wherein a sensor monitors a color print output for on-line construction of an analytical model of printer operation. More particularly, the invention relates to system controls for modeling the printer by implementating an adaptive algorithm for estimation of analytical model parameters based upon processing of a relatively small number of control samples, target colors or other input signals, whereby the model is then useful for calibrating, diagnosing or standardizing operations of the printer.
Color correction and/or control should not be confused with color registration systems and sensors for insuring that colors are positioned properly, printed accurately, superposed correctly and/or adjacent to one another.
In today""s business and scientific world, color has become essential as a component of communication. Color facilitates the sharing of knowledge and ideas. Companies involved in the development of digital color print engines are continuously looking for ways to improve the total image quality of their products. One of the elements that affects image quality is the ability to consistently produce the same quality image output on a printer from one day to another, from one week to the next, month after month. Colors on a printer tend to drift over time due to ink/toner variations, temperature fluctuations, type of media used, environment, etc. There has been a long felt commercial need for efficiently maintaining print color predictability, particularly as electronic marketing has placed more importance on the accurate representation of merchandise in illustrative print or display media.
Color perception is a psychological and physiological phenomenon that involves three elements: light, object and observer. Color changes as light, medium (i.e., paper, monitor) and observer interact. Color may be perceived differently under different types of lighting. Light sources that affect color include incandescent and fluorescent light. The first makes color seem more red and orange while the second emphasizes green and yellow tones. Different types of media also affect color perception. Paper is a medium that reflects color ink. In other cases the medium can be transmissive or emissive. Transparencies are an example of a transmissive medium while a computer monitor is emissive. The third element in the phenomenon is the observer. Different people may see the same color slightly differently. In order to characterize color image quality, the interaction of these elements must be understood so that when colors are intended to be matched, i.e., monitor to printer, scanner to printer, etc., acceptable appearance results.
For automatic control systems spectral data is often used to represent color perception as a pattern of wavelengths that leave the object before being interpreted by a viewer. Spectral data defines color independent of light and observer influence. A spectrophotometer is a sensing device used to measure spectral data.
There are different ways of representing color. One way color is described consists of the following parameters: hue, lightness and saturation. Hue represents the actual color wavelength (red, blue, etc.), lightness corresponds to the white content while saturation captures the richness or amplitude in color. Another way of describing color uses the three dominant primary colors red, blue and green (RGB). By combining these primary colors, in different intensities, most colors visible to humans can be reproduced. Monitors and scanners use the additive RGB color process. Printers use the subtractive CMYK (cyan, magenta, yellow and black) color process based on light reflected from inks coated on a substrate. The color representations described above fail to reproduce color predictably because they are observer or device dependent.
The functional models presented in this specification use a device independent color space to consistently track a set of target colors. L*, a*, b* are the CIE (Commission Internationale de L""xc3xa9clairage) color standards utilized in the modeling. L* defines lightness, a* corresponds to the red/green value and b* denotes the amount of yellow/blue.
On-line model prediction is also known as xe2x80x9csystem identificationxe2x80x9d in automatic controls literature. It is the terminology used for the process of characterizing a given control system. Characterization of the system can be done in two ways; non-parametric and parametric. In non-parametric system identification, the profile of the device can be measured by printing specific target colors as specified by the known standards. This profile is used as it is (without constructing any model of the device) while making rendering decisions/viewing of the customer colors on the monitor. This is one time measurement and does not use the historical information to construct any model. Whereas in the parametric system identification, predetermined target colors can be printed as chronological jobs in the banner sheet/header sheet or else the target colors can be extracted from the customer image and measured either by measuring straight from the output image or by rendering subset of customer colors as target color patches in banner or header page. (c.f. copending Xerox application D/99511Q1xe2x80x94L. K. Mestha, inventor, for a control system using dual mode banner color test sheets, herein incorporated by reference.) Using the target colors and their measured counterparts, parameters of the model are adjusted on-line during each measurement. The intention in the parametric system identification is to adjust the parameters of the model and refine it over time by using past and present color data so that the model is what customers can use in their desktops. If such models can be incorporated on the smart color sensor, then the model can be exported to customer workstations.
As business and scientific environments continue to require increasingly complex printing capabilities, and especially more consistent and accurate color matching outputs, there is a continuing need for improved on-line modeling and calibrating of color printing devices. Prior systems which have suggested color modeling (c.f., U.S. Pat. No. 5,612,902 to Stokes) have been unable to recursively converge the model parameters continuously and efficiently. Current needs are better served with analytical model processing in a way that accurate parameters of a parametric model can be quickly identified through a recursive computation scheme. In a network printing environment such a need is particularly apparent for multiple prints which can come from different sources to different printers all networked to one another. Multiple printers could be of the same or different color marking technologies/colorants/materials linked to the same or different color gamuts. The multiple printer outputs being compared could even come from different types of printers, such as black and white, highlight color and process color printers. The principal problems exist when all of the prints from these different types of printers will not match or even be consistent from day-to-day. The problem domain grows by several orders of magnitude when the images are viewed under different lighting sources/viewing angles and are printed on different paper with non-optimum originals. Color differences produced on documents could also be due to stochastic errors on the images as they are produced by the devices of varying types, technology and media.
The subject invention is particularly useful to provide solutions to the foregoing color problems for a wide range of color workflow practices and particularly in customer environments with complex printing requirements. Printing and product enhancements are provided that would enable customers to manipulate color documents on a screen before even printing/displaying an output on different output devices in ways that improve the productivity of a current workflow by taking advantages of output color sensors constructed within the printing devices.
The subject invention exploits a key enabling factor for these operational advantages by constructing and maintaining a current analytical model of the reproducing device operation (also known as the device profile or characteristic, input-output model within the reproducible color). A knowledge of the characteristic/model of the device at the time of manipulating color documents is a precise dynamic model of the marking device. The subject invention provides a new and improved method for constructing such a dynamic model by using a color sensor mounted within the output device. When a customer has a need to accurately match the colors displayed or printed on various output devices, such as monitors (CRT, LCD, etc.) and printers (xerographic, ink jet, ionographic, etc.), he/she can get the most current analytical model of the particular output device by simply making a call request to the device or the repository where the analytical model is stored. Prior art systems have been known to use such color sensors offline to measure the most current profile of the device, e.g., an ICC profile. The subject invention improves on such sensor utilization by not only the mere acquisition of the ICC profile, but also the extraction of a dynamic parametric model using color information accumulated over a period of time. In other words, a recursive model of the printer is generated which by nature will be more accurate and up-to-date than a mere profile with one time measurement. A customer can use the analytical model in a soft-proofing package to make aesthetic decisions or rendering decisions across devices to produce the best reproducible colors vivid or less vivid pictures, etc. Without having such an accurate dynamic model of the device, customer intentions/preferences cannot be met accurately, even though they can be conceived visually on a monitor. The subject invention provides for the creation and the effective wide spread use of such dynamic analytical models for devices that will enable consistent image reproduction initiated remotely across a system network.
In accordance with the present invention, there is provided a method and apparatus for on-line prediction of an accurate analytical model of a dynamically varying color reproduction device such as a printer. The device includes a sensor for detecting a signal representative of the device color output. The form of the analytical model is predetermined and preferably comprises a parametrical model comprising initially a random set of parameters. An image is produced with the device in response to an input signal. The sensor senses an output signal from the generated image representative of the image. The analytical model computes a model output in response to the same input signal. A difference or error is determined between the image output signal and the model output. Updated parameters are generated for the model from the prediction algorithm utilizing the determined error and are then applied as replacements to the model. The model is inverted and a subsequent input signal is transformed by the inverted model for adjusting the subsequent input signal therewith. The foregoing steps are recursively executed with a plurality of subsequent input signals until the determined error is less than a preselected value whereby the analytical model comprises an accurate representation of the device operation.
In accordance with another aspect of the present invention, the foregoing steps for generating the accurate set of parameters for the analytical model can be utilized to create a model for accurately calibrating a color printer during run time operation with only a relatively few number of parameter updating computations. The transforming of subsequent input signals with the inverse of the updated parametric model continually converges the parameter set until an accurate updated parametric model is determined. The subsequent images produced by the printer have input signals which are then calibrated by the inverse of the updated parametric model so that transformation of the input signal through the color printer results in a precise and accurate transformation of the input signal to the desired output signal.
In accordance with a more limited aspect of the present invention, the parameter adjustment through the generating of the updated sets of parameters utilizes a recursive convergent least square estimation algorithm incorporating the determined error between the image output signal and the model output.
In accordance with yet another more limited aspect of the present invention, the adjustment can be implemented with either known selected test target signals as the input signal, or normal print/display path operating input signals.
In accordance with another aspect of the present invention, the analytical model, after successful updating, can be stored as a diagnostic program in a network system comprising a plurality of the reproducing devices. Devices in the network which generate an error greater than a certain selected value with the error computation steps above, are then recalibrated or repaired.
One advantage obtained by use of the present invention is an on-line model prediction process capable of constructing an accurate parametric model of a reproducing device. The model construction process is neither operator or human assisted.
Another advantage of the subject invention is that the models can be ported to a web or network system for communication to customer desktops on demand. The model can be embedded as a routine call request in a new service paradigm of future systems. Such calls can be part of document portals.
Another advantage of the subject invention is that the constructed analytical model can be used to diagnose reproducing device deviations from normal operating limits.
Yet another operational advantage of the invention is that the model construction can be realized with a relatively limited number of input signals for processing due to expeditious convergence of model parameters accurately representing device operation for the particular model selected.
Other benefits and advantages for the subject new methods and systems will become apparent to those skilled in the art upon a reading and understanding of this specification.